Neurosurgeon Implants a Computer into His Brain

Phil Kennedy was unable to find the right subject for his project. The goal of the project was to make it so that severely disable patients would be able to speak using their brains alone, even though their mouths did not work, but better than what Stephen Hawking uses.

Even if he had, it would have been difficult to get government approval for what he intended to do, which was install a computer on the brain and do research from there. The FDA was unsure about previous experiments that had been done, and didn’t want to approve any more implants. Thus, he decided to do it himself.

Kennedy flew out of the country, to Belize, where he had another surgeon implant a computer on his brain. Two experiments later and he was able to begin looking at the data from his own brain. The foremost problem with this, of course, was that he was not the target of the technology he was trying to develop, as he already had full control of his ability to speak.

Kennedy then faced another serious problem that cut his research short. He had planned to live with the implants for years, but the wound from the surgery never healed completely. This meant that Kennedy was in danger, and needed to have the implants removed. It cost nearly $100,000 to have the implants removed. However, Kennedy was pleased with the results he was able to get while the implants were installed.

Neural computing like that of Phil Kennedy and cybernetics is definitely a part of the future. Such technologies could potentially return the gift of sight to the blind, re-enable those who’ve lost motor functions, and do many other things that are not possible with modern medicine or surgery. These are the medical applications, but other applications are possible as well, such as the use of man-machine technology in the military. Creating body suits that are both protective and enhancing could become a priority of the Defense Department. This would create a new industry.

Other robotics have already been employed by the military, with the Marine Corps recently employing the help of a robotic pack animal type of creation. While some fear a future with artificial intelligence, others look forward to a time where average people can afford to have a maid that doesn’t have kids to get home to after work. A robotic future could also mean a future where robots do most of the labor, creating significant economic impact for the working class.

This is part of what has some in the technology and science and other fields championing a universal basic income in the future. Whether such ideas ever actually take root or always flounder in debate, that remains to be seen. What is clear is that even in societies where people fundamentally have more than they need, people continue to work hard and produce. It seems that if most of the labor of society were being done by robots, people would continue to find useful ways to spend their time.

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5 stars on average, based on 1 rated postsP. H. Madore has covered the cryptocurrency beat over the course of hundreds of articles for Hacked's sister site, CryptoCoinsNews, as well as some of her competitors. He is a major contributing developer to the Woodcoin project, and has made technical contributions on a number of other cryptocurrency projects. In spare time, he recently began a more personalized, weekly newsletter at http://ico.phm.link

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This article is unfortunately nearly information-free, possibly describing anything from a bunch of crude electrodes picking up brain signals all the way to a supercomputer seamlessly integrated into the brain’s fabric in order to create a new class of being. From a quick search for more on this, it would appear that reality is heavily slanted towards the former end of the spectrum, as this chip was merely recording activity of 65 neurons when the brain’s owner spoke or thought about speaking.

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The End of Human Money Managers

Quantitative Easing by central banks around the world has led to dramatic changes in the money management industry over the past six years. Not only have we seen increasing regional differences, but stock picking has also become more difficult as the money injected into the markets by central banks has lifted pretty much everything, regardless of valuation and the future potential of the asset.

Investors have become impatient and highly demanding as a result of years of low interest rates. Old mutual funds are being swapped out for new, better ones at a record pace as investors hunt for higher ROI. Passive income has become a trend, and ETF’s and automated investment strategies are getting more and more popular as a result.

How do money managers attract capital?

There are three main factors that determine how much capital a money manager is able to attract from investors:

Track record

Strategy

Technology

Changes in any of these factors can have a big impact on investors’ willingness to let the fund manager keep the money they have already invested with him, or receive new money.

Technology has been a very important driver over the past few years. Data-driven, or quantitative funds are gaining an ever-increasing market share in the money management space. This is happening because more and more people are realizing the obvious benefits that this type of money management has to offer.

Investors increasingly prefer the robustness, speed, and predictability that automated money management can provide. When it comes to robustness, we are referring to both the physical and psychological aspect of it.

Humans vs. robots

Humans are pretty much the opposite of “robust,” in the true sense of the word. Our emotional state on any given day can make us react to things in different ways than we otherwise would have done, potentially leading to critical mistakes for a trader.

As humans, we may miss trading opportunities in the market because we came in late, took a day off, or simply didn’t pay attention at any given moment.

Robots are obviously not affected by fatigue and lack of focus. For example, a robot can monitor the stock or cryptocurrency market and trade just like a human trader would do, with the only difference being that the former (arguably) does it better and never needs to rest.

Thanks to the high computing power available today, robots can collect, verify, analyze, and react to opportunities long before a human will even understand that such opportunities exist.

Data-driven approach to fund management is taking over

A recent ranking by Institutional Investor Magazine revealed that out of the world’s 100 biggest hedge funds, five of the top six spots were held by data-driven funds.

On first place was Ray Dalio’s Bridgewater Associates with $122.3 billion under management. In 2016, Bridgewater grew the amount of money under management by 17%.

Renaissance Technologies, the company known for having hundreds of mathematicians, physicists, and coders on their payroll, came in fourth with $43 billion.

Two Sigma, which is also well-known for using technologies like AI and machine learning, came in fifth with $39 billion under management. Their increase from the year before was 28%.

According to Barclays, $500 billion are now invested in purely data-driven funds, while JP Morgan claims that data-driven trading strategies accounts for a whopping 90% of global trading volumes in stocks.

The core objective of any money manager is always to follow the money. That’s why we are seeing a race right now by the big players in the industry to use words like “technology-driven,” “artificial intelligence,” and so on. Whether or not that is true is not always a concern for them.

Money managers are destined to unemployment

Those who are really in trouble because of this huge change are the money managers themselves. Most of them will likely lose their jobs over the next few years. There is simply very little need for their very expensive services anymore, as robots are able to do the same thing in a much cheaper and more consistent way.

As legendary investors Jim Rogers predicted a few years ago, the stock brokers will become broke and the farmers are going to be driving Lamborghinis. Maybe there will finally be some truth to this.

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4.1 stars on average, based on 19 rated postsFredrik Vold is an entrepreneur, financial writer, and technical analysis enthusiast. He has been working and traveling in Asia for several years, and is currently based out of Beijing, China. He mainly follows the stock and forex markets, and is always looking for the next great alternative investment opportunity.

Bitcoin Giant Bitmain Enters the High Stakes AI Race

The Sophon, named for a fictional proton-sized supercomputer, could be the tool to train neural networks in data centers worldwide. It is the latest project being developed by Bitmain Technologies Ltd., the bitcoin mining giant that has carved out a dominant position in bitcoin mining.

Such chips, called application-specific integrated circuits (ASICs), could unleash a new wave of distributed computing, according to Michael Bedford Taylor, a University of Washington professor who studies bitcoin mining and chips.

Sophon is due to debut before the end of the year.

Bitmain Has The Know-How

Bitmain has the background to play a role in the expanding artificial intelligence industry. The company designs the silicon that goes in bitcoin mining equipment, assembles the machines and sells them worldwide, in addition to its own bitcoin mining operation and the ones that it manages for other mining pools.

Jihan Wu, the co-founder of Bitmain, supports the New York Agreement that seeks to double the bitcoin block size under the SegWit2X proposal, a move that some in the bitcoin community view as an attempt to give the miners control over bitcoin.

Some also believe Wu was behind the recent bitcoin split known as bitcoin cash, which at least one of Bitmain’s miners supported, a contention that Wu has denied. Wu points out that he was among the supporters of Bitcoin Unlimited, an earlier bitcoin scaling proposal that did not get activated.

Why Wu Supports Forks

Wu nonetheless said splits should be allowed. He said a fork is inevitable since people in the bitcoin community do not agree on how to best scale bitcoin.

Wu met Micree Zhan, Bitcoin’s co-founder, when Zhan was running DivaIP in 2010, a company that made a device that allowed a user to stream a TV show on a computer screen.

In 2011, Wu needed a chip designer to build a mining operation and approached Zhan. Zhan first designed an ASIC to run SHA-256, the cryptographic calculation used in bitcoin, at maximum efficiency. It took him six months to finish the job. His first rig, Antminer S1, was ready in November 2013.

Bitmain felt the sting of the 2014 Mt. Gox meltdown. But by 2015, bitcoin’s price bottomed out and later recovered. In the meantime, Bitmain introduced its Antminer S5.

Ready To Take On Google

Bitmain has since developed a deep learning chip with improved efficiency. Users will be able to build their own models on the ASICs, enabling neural networks to deliver results at a faster pace. Google’s DeepMind unit used this technique to train its AlphaGo artificial intelligence.

Bitmain plans to sell the chips to any company looking to train its own neural nets, including firms like Alibaba, Tencent and Baidu. Bitmain could build its own data centers with thousands of deep learning rigs, renting out the computation power to clients the way it does with bitcoin mines.

Professor Taylor said companies like Bitmain that have excelled in bitcoin mining could take on the Googles and Nvidias since they have developed the skills to survive in an ultra-competitive and highly commoditized industry, and have the system level design expertise and the ability to reduce data center costs.

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4.8 stars on average, based on 3 rated postsLester Coleman is a veteran business journalist based in the United States. He has covered the payments industry for several years and is available for writing assignments.

Using an AI system video from the body cameras worn on the officers at the scene will relay back to experts who can guide the officers by making virtual notes which the officers will be able to see via a smartphone or head-mounted device.

So by viewing the footage that is sent from a camera on the police vest, a chemical specialist in one location can view it while a forensic scientist in another location can too. The system is similar to the popular Pokémon Go smartphone game that has grabbed the attention of millions of people around the world.

Not Suitable for Making an Arrest

However, while the technology may prove beneficial in providing an extra pair of eyes for investigating crime scenes, when it comes to making an actual arrest the technology is not suitable for that just yet.

According to Nick Koeman, innovation adviser from the National Police of the Netherlands, the officers undertaking the AI system trial found the extra information distracting.

Of course, some may simply say that ensuring a complete team is at the scene of a crime would be more beneficial for an investigation; however, that is not always possible due to budget cuts and time constraints.

As such the use of an AI system that can cut down on the number of people involved at a crime scene without sacrificing on the required thoroughness could potentially provide the answer that many police departments are searching for.

Not only that, but by reducing the number of people at a scene it cuts the potential possibility of contaminating evidence. The use of AI gives people the chance to assess the evidence and discover additional clues without being at the crime scene.

AI could also help in court cases by helping to recreate a scene for a jury, but as Michael Buerger, professor of criminal justice at Bowling Green State University in Ohio states, legal challenges are likely to raise when augmented reality (AR) is used in the courts.

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Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink.

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